How to start with Machine Learning and Neural Networks

Many people want to start their studies in neural networks and machine learning as a whole. So I decided to make a guide that I’m using to study these two technologies.

First, you have to choose a language. I chose Python.
Python can be downloaded through the Anaconda distribution, which in addition to packing in a functional way, still has a control panel for installations from other libraries: https://www.anaconda.com/download.

Now, you’re going to need an IDE. Do I currently use Visual Studio Code https://code.visualstudio.com.

Don’t know Python?

Follow the Basic Tutorial category at https://www.tutorialspoint.com/python/index.htm. It’s a start. There are many good courses in Coursera, Udemy, and EDX.

After doing the tutorial, some libraries are a “must have” for machine learning:

Do you want a list of datasets with the current state of art and other applications for these bases (including the MNIST and CIFAR-10)?

You have it here: http://rodrigob.github.io/are_we_there_yet/build/

Do you find a base you want to work on?

You want to know how people are solving a particular problem? So get ready to read papers, get ready to read LOTS OF PAPERS, and they’ll probably be posted here: https://arxiv.org.

Have you got any questions about how some network works?

Probable that the Siraj Raval has already explained: https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A

If you want any more reliable sites for explanations of any network / architectural/problem solution that you have, I recommend the O’ Reilly Media (https: //www.oreilly.com/) and Medium (https: //medium.com/)

It also has the following list:

http://ai.google/education-Google course

https://see.stanford.edu/course/cs229-Classic CS229, milestone in the area, but quite extensive.

https://br.udacity.com/courses/machine-learning

-There are several courses in Udacity, beyond the nanodgrees, some paid, other free, it’s even difficult to choose which to study if you have any other questions about what or how to search, remember: Google Is Your Friend.